이 페이지의 최신 내용은 아직 번역되지 않았습니다. 최신 내용은 영문으로 볼 수 있습니다.

# 다변량 회귀

다변량 응답 변수를 사용하는 선형 회귀

## 함수

 `mvregress` 다변량 선형 회귀 `mvregresslike` Negative log-likelihood for multivariate regression `polytool` Interactive polynomial fitting `polyconf` Polynomial confidence intervals `plsregress` Partial least-squares regression

## 예제 및 방법

Set Up Multivariate Regression Problems

To fit a multivariate linear regression model using `mvregress`, you must set up your response matrix and design matrices in a particular way.

Multivariate General Linear Model

This example shows how to set up a multivariate general linear model for estimation using `mvregress`.

Fixed Effects Panel Model with Concurrent Correlation

This example shows how to perform panel data analysis using `mvregress`.

Longitudinal Analysis

This example shows how to perform longitudinal analysis using `mvregress`.

부분 최소제곱 회귀 및 주성분 회귀

이 예제에서는 부분 최소제곱 회귀(PLSR) 및 주성분 회귀(PCR)를 적용하는 방법을 보여주고 이 두 방법의 효과를 설명합니다.

## 개념

Multivariate Linear Regression

Large, high-dimensional data sets are common in the modern era of computer-based instrumentation and electronic data storage.

Estimation of Multivariate Regression Models

When you fit multivariate linear regression models using `mvregress`, you can use the optional name-value pair `'algorithm','cwls'` to choose least squares estimation.

Partial Least Squares

Partial least squares (PLS) constructs new predictor variables as linear combinations of the original predictor variables, while considering the observed response values, leading to a parsimonious model with reliable predictive power.